@unpublished{SmallBigDiscovering2017a,
	title = {The small from the {Big}: discovering models and mechanisms with machine learning},
	copyright = {All rights reserved},
	abstract = {This paper proposes a new discussion on  Big Data” as the primary source of model-building in science, when the computational architecture used is machine learning or evolutionary algorithms (and possibly a combination of them). Is Big Data mining a proper method of discovering and building models? The focus is on discovery and building (rather than justification or confirmation) of existing theories or models; and on relevant epistemic and pragmatic aspects of these  computational architectures (rather than on the quantitative features of Big Data). Two competing (or, more mildly put, complementing) types of models used in biology and cognitive science are scrutinized: mechanism modeling and
computational modeling (mostly network modeling and dynamical modeling).
This paper aims to show that far from becoming irrelevant in the Big Data era, network models and mechanisms can be discovered and built from Big Data. The argument is based on the concept of patterns in data, discussed in the context of the relation between data and models (Bogen, Woodward, McAllister). It relates it then to the concept of “small patterns” in Big Data (Floridi) as hidden aspects of mechanistic models, hard to fathom by scientists.
Machine learning, as a tool to categorize and characterize real patterns is assessed in the context of mechanistic and network models. Evolutionary computation is assessed as a method to optimize the search for mechanisms and complex networks.
To compare and contrast the mechanistic account and its alternatives, this argument builds on two
concepts central to all approaches: modularity (as related to decomposability), and organization
(Bechtel, Darden, Craver), which both come in degrees and can be discovered through machine learning or evolutionary computation in Big Data (cf. E. Ratti and W. Pietsch). The paper concludes with the
claim that Big Data, when it qualifies as scientific evidence, most likely has and will have a fundamental impact on the way we discover and build computational models in science.},
	month = feb,
	year = {2017},
}

{"_id":"srHNbByTpy9RbsJMb","bibbaseid":"anonymous-thesmallfromthebigdiscoveringmodelsandmechanismswithmachinelearning-2017","authorIDs":[],"bibdata":{"bibtype":"unpublished","type":"unpublished","title":"The small from the Big: discovering models and mechanisms with machine learning","copyright":"All rights reserved","abstract":"This paper proposes a new discussion on Big Data” as the primary source of model-building in science, when the computational architecture used is machine learning or evolutionary algorithms (and possibly a combination of them). Is Big Data mining a proper method of discovering and building models? The focus is on discovery and building (rather than justification or confirmation) of existing theories or models; and on relevant epistemic and pragmatic aspects of these computational architectures (rather than on the quantitative features of Big Data). Two competing (or, more mildly put, complementing) types of models used in biology and cognitive science are scrutinized: mechanism modeling and computational modeling (mostly network modeling and dynamical modeling). This paper aims to show that far from becoming irrelevant in the Big Data era, network models and mechanisms can be discovered and built from Big Data. The argument is based on the concept of patterns in data, discussed in the context of the relation between data and models (Bogen, Woodward, McAllister). It relates it then to the concept of “small patterns” in Big Data (Floridi) as hidden aspects of mechanistic models, hard to fathom by scientists. Machine learning, as a tool to categorize and characterize real patterns is assessed in the context of mechanistic and network models. Evolutionary computation is assessed as a method to optimize the search for mechanisms and complex networks. To compare and contrast the mechanistic account and its alternatives, this argument builds on two concepts central to all approaches: modularity (as related to decomposability), and organization (Bechtel, Darden, Craver), which both come in degrees and can be discovered through machine learning or evolutionary computation in Big Data (cf. E. Ratti and W. Pietsch). The paper concludes with the claim that Big Data, when it qualifies as scientific evidence, most likely has and will have a fundamental impact on the way we discover and build computational models in science.","month":"February","year":"2017","bibtex":"@unpublished{SmallBigDiscovering2017a,\n\ttitle = {The small from the {Big}: discovering models and mechanisms with machine learning},\n\tcopyright = {All rights reserved},\n\tabstract = {This paper proposes a new discussion on Big Data” as the primary source of model-building in science, when the computational architecture used is machine learning or evolutionary algorithms (and possibly a combination of them). Is Big Data mining a proper method of discovering and building models? The focus is on discovery and building (rather than justification or confirmation) of existing theories or models; and on relevant epistemic and pragmatic aspects of these computational architectures (rather than on the quantitative features of Big Data). Two competing (or, more mildly put, complementing) types of models used in biology and cognitive science are scrutinized: mechanism modeling and\ncomputational modeling (mostly network modeling and dynamical modeling).\nThis paper aims to show that far from becoming irrelevant in the Big Data era, network models and mechanisms can be discovered and built from Big Data. The argument is based on the concept of patterns in data, discussed in the context of the relation between data and models (Bogen, Woodward, McAllister). It relates it then to the concept of “small patterns” in Big Data (Floridi) as hidden aspects of mechanistic models, hard to fathom by scientists.\nMachine learning, as a tool to categorize and characterize real patterns is assessed in the context of mechanistic and network models. Evolutionary computation is assessed as a method to optimize the search for mechanisms and complex networks.\nTo compare and contrast the mechanistic account and its alternatives, this argument builds on two\nconcepts central to all approaches: modularity (as related to decomposability), and organization\n(Bechtel, Darden, Craver), which both come in degrees and can be discovered through machine learning or evolutionary computation in Big Data (cf. E. Ratti and W. Pietsch). The paper concludes with the\nclaim that Big Data, when it qualifies as scientific evidence, most likely has and will have a fundamental impact on the way we discover and build computational models in science.},\n\tmonth = feb,\n\tyear = {2017},\n}\n\n","key":"SmallBigDiscovering2017a","id":"SmallBigDiscovering2017a","bibbaseid":"anonymous-thesmallfromthebigdiscoveringmodelsandmechanismswithmachinelearning-2017","role":"","urls":{},"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"unpublished","biburl":"https://api.zotero.org/users/125019/collections/5U7LGU73/items?key=ewW1RaRuE0S1jNousA0Xuz9X&format=bibtex&limit=100","creationDate":"2019-04-24T14:14:45.734Z","downloads":0,"keywords":[],"search_terms":["small","big","discovering","models","mechanisms","machine","learning"],"title":"The small from the Big: discovering models and mechanisms with machine learning","year":2017,"dataSources":["GPpRKkvJnTEshZncz","wjn8cK5LeFh95gDh5","387WCqHy3dhc6wxAM","m6XEq4NgafoWkPhJH","3DoWMBkcaKTznZMsx"]}